Dental cad automation using deep learning

ABSTRACT

Computer-implemented methods for generating a 3D dental prosthesis model are disclosed herein. The methods comprise training a deep neural network to generate a first 3D dental prosthesis model using a training data set; receiving a patient scan data representing at least a portion of a patient&#39;s dentition; and generating, using the trained deep neural network, the first 3D dental prosthesis model based on the received patient scan data.

TECHNICAL FIELD

The disclosure relates generally to the field of dental computer-aideddesign (CAD), and specifically to dental CAD automation using deeplearning.

BACKGROUND

Recently, CAD/CAM dentistry (Computer-Aided Design and Computer-AidedManufacturing in dentistry) has provided a broad range of dentalrestorations, including crowns, veneers, inlays and onlays, fixedbridges, dental implant restorations, and orthodontic appliances. In atypical CAD/CAM based dental procedure, a treating dentist can preparethe tooth being restored either as a crown, inlay, onlay or veneer. Theprepared tooth and its surroundings are then scanned by a threedimensional (3D) imaging camera and uploaded to a computer for design.Alternatively, a dentist can obtain an impression of the tooth to berestored and the impression may be scanned directly, or formed into amodel to be scanned, and uploaded to a computer for design.

Current dental CAD still relies heavily on manual labor. Minimizing theamount of manual labor involved in CAD of those restorations is of highinterest. The ultimate goal is to provide a fully-automatic solutionwhich is capable to deliver acceptable designs without humaninterference. In order to build such a highly autonomous dental CADsystem, high level expertise needs to be integrated into the software.One way to do that is to build a comprehensive set of rules that wouldinclude all the nuances known to the experienced dental professionalsand formulating it in a way machine can understand. However, each dentalrestoration is unique and certain decisions that are made by dentaltechnicians are very hard to define rigorously. Therefore this is a verytedious task, and obviously feasible only when this set of rules can beprovided.

A different approach which has recently gained popularity in the MachineLearning (ML) community is based on an idea to build a system which iscapable of learning from a large number of examples without explicitformulation of the rules. This method is commonly referred to as DeepLearning (DL), which is used with certain amount of success in speechrecognition (e.g., Siri), computer vision (e.g., Google+), automaticoffering (e.g., Amazon) to name a few. Availability of large amounts ofdata and computational power provide the ability to address problemsthat seemed intractable just a couple years ago. DL provides the abilityto train very large Deep Neural Networks (DNNs) using massive amounts ofdata.

SUMMARY

Example embodiments of methods and computer-implemented systems forgenerating a 3D model of a dental prosthesis using deep neural networksare described herein. Certain embodiments of the methods can include:training, by one or more computing devices, a deep neural network togenerate a first 3D dental prosthesis model using a training data set;receiving, by the one or more computing devices, a patient scan datarepresenting at least a portion of a patient's dentition; andgenerating, using the trained deep neural network, the first 3D dentalprosthesis model based on the received patient scan data.

The training data set can include a dentition scan data set withpreparation site data and a dental prosthesis data set. A preparationsite on the gum line can be defined by a preparation margin or marginline on the gum. The dental prosthesis data set can include scannedprosthesis data associated with each preparation site in the dentitionscan data set.

The scanned prosthesis can be scans of real patients' crowns createdbased on a library tooth template, which can have 32 or more toothtemplates. The dentition scan data set with preparation site data caninclude scanned data of real preparation sites from patients' scanneddentition.

In some embodiments, the training data set can include a naturaldentition scan data set with digitally fabricated preparation site dataand a natural dental prosthesis data set, which can include segmentedtooth data associated with each digitally fabricated preparation site inthe dentition scan data set. The natural dentition scan data set canhave two main components. The first component is a data set thatincludes scanned dentition data of patients' natural teeth. Data in thefirst component includes all of the patients' teeth in its natural andunmodified digital state. The second component of the natural dentitionscan data is a missing-tooth data set with one or more teeth removedfrom the scanned data. In place of the missing-tooth, a DNN fabricatedpreparation site can be placed at the site of the removed tooth. Thisprocess generates two sets of dentition data: a full and unmodifieddentition scan data of patients' natural teeth; and a missing-tooth dataset (natural dental prosthesis data set) in which one or more teeth aredigitally removed from the dentition scan data.

In some embodiments, the method further includes generating a full archdigital model and segmenting each tooth in the full arch to generatenatural crown data for use as training data. The method can alsoinclude: training a second deep neural network to generate a second 3Ddental prosthesis model using a natural dentition scan data set withdigitally fabricated preparation site data and a natural dentalprosthesis data set; generating, using the second deep neural network,the second 3D dental prosthesis model based on the received patient scandata; and blending together features of the first and second 3D dentalprosthesis models to generated a blended 3D dental prosthesis model.

In some embodiments, natural dentition scan data can be selected tosimilarly match the patient's profile such as gender, age, ethnicity,diet, lifestyle, etc. Once the patient's profile is determined, naturaldentition scan data having similar or identical profile can be selectedto train deep neural networks to generate a customized 3D dentalprosthesis.

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the disclosed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of a system for recognizing dentalinformation from scan data of patients' dentitions and for designingdental restorations in accordance with some embodiments of thedisclosure.

FIG. 2 is a high-level block diagram of a computing device in accordancewith some embodiments of the present disclosure.

FIG. 3 is a flow chart of a process for generating a 3D dentalprosthesis model using a deep neural network in accordance with someembodiments of the present disclosure.

FIG. 4 is a high-level block diagram showing structure of a deep neuralnetwork in accordance with some embodiments of the present disclosure.

FIG. 5 is a graphic representation of input and output to a deep neuralnetwork in accordance with some embodiments of the present disclosure.

FIG. 6A-B are flow diagrams of methods for training a deep neuralnetwork to generate a 3D dental prosthesis in accordance with someembodiments of the present disclosure.

FIG. 7-8 are flow charts of methods for training deep neural networks togenerate a 3D dental prosthesis in accordance with some embodiments ofthe present disclosure.

FIGS. 9A-9B illustrate examples crowns generated using deep neuralnetworks in accordance with some embodiments of the present disclosure.

FIG. 10A-10B illustrates a graphic user interfaces used totransfer/combine features from one dental prosthesis design to anotherin accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems for recognizing dental information or features from scan data ofpatients' dentitions and for designing dental restorations using deepneural networks are described below. In the following descriptions, forpurposes of explanation, numerous specific details are set forth toprovide a thorough understanding of the invention. However, it will beapparent to one skilled in the art that the invention can be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form in order to avoid obscuring theinvention.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

Some portions of the following detailed description are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the methods used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared or otherwise manipulated. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following disclosure,it is appreciated that throughout the disclosure terms such as“processing,” “computing,” “calculating,” “determining,” “displaying” orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system's memories or registersor other such information storage, transmission or display devices.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may be a general-purpose computer selectivelyactivated or reconfigured by a computer program stored in the computer.The invention may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment including both hardwareand software elements. In some embodiments, the invention is implementedin software comprising instructions or data stored on acomputer-readable storage medium, which includes but is not limited tofirmware, resident software, microcode or another method for storinginstructions for execution by a processor.

Furthermore, the invention may take the form of a computer programproduct accessible from a computer-usable or computer-readable storagemedium providing program code for use by, or in connection with, acomputer or any instruction execution system. For the purposes of thisdescription, a computer-usable or computer readable storage medium isany apparatus that can contain, store or transport the program for useby or in connection with the instruction execution system, apparatus ordevice. The computer-readable storage medium can be an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system(or apparatus or device) or a propagation medium. Examples of a tangiblecomputer-readable storage medium include, but are not limited to, asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk, an optical disk, an EPROM, an EEPROM, a magneticcard or an optical card, or any type of computer-readable storage mediumsuitable for storing electronic instructions, and each coupled to acomputer system bus. Examples of optical disks include compact disk-readonly memory (CD-ROM), compact disk-read/write (CD-R/W) and digital videodisc (DVD).

A data processing system suitable for storing and/or executing programcode includes at least one processor coupled directly or indirectly tomemory elements through a system bus. The memory elements may includelocal memory employed during actual execution of the program code, bulkstorage and cache memories providing temporary storage of at least someprogram code in order to reduce the number of times code must beretrieved from bulk storage during execution. In some embodiments,input/output (I/O) devices (such as keyboards, displays, pointingdevices or other devices configured to receive data or to present data)are coupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the data processing system toallow coupling to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modem and Ethernet cards are just examples of the currentlyavailable types of network adapters.

Finally, the algorithms and displays presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may be used with programs in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will appear from thedescription below. It will be appreciated that a variety of programminglanguages may be used to implement the teachings of the invention asdescribed herein.

The figures and the following description describe certain embodimentsby way of illustration only. One skilled in the art will readilyrecognize from the following description that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles described herein. Reference will now bemade in detail to several embodiments, examples of which are illustratedin the accompanying figures. It is noted that wherever practicablesimilar or like reference numbers may be used in the figures to indicatesimilar or like functionality.

System Overview

Exemplary embodiments of methods and systems for recognizing dentalinformation or features from scan data of patients' dentitions and fordesigning dental restorations using deep neural networks are describedherein. The computer-implemented methods of designing dentalrestorations described herein use an electronic image of at least aportion of a patient's oral situation as a starting point for the designprocess. In some embodiments, the electronic image is obtained by adirect intraoral scan of the patient's teeth. This typically takesplace, for example, in a dental office or clinic and be performed by adentist or dental technician. In other embodiments, the electronic imageis obtained indirectly by scanning an impression of the patient's teeth,by scanning a physical model of the patient's teeth, or by other methodsknown to those skilled in the art. This typically takes place, forexample, in a dental laboratory and be performed by a laboratorytechnician. Accordingly, the methods described herein are suitable andapplicable for use in chair side, dental laboratory, or otherenvironments. Using the electronic image, a computer-implemented dentalinformation or features recognition system is used to automaticallyidentify useful dental structure and restoration information and detectfeatures and margin lines of dentition, thus facilitating automaticdental restoration design and fabrication in following steps.

In some embodiments, a plurality of scans (e.g., 3-5 scans per quadrant)is performed in order to obtain a suitable image of the patient'sanatomy. For example, an occlusal, lingual, and buccal scan may be takenof both the preparation and the opposing jaws. Then, a single scan withthe jaws in occlusion may be taken from the buccal perspective toestablish the proper occlusion relationship between the preparation jawand the opposing jaw. Additionally, in some embodiments, interproximalscans are added to capture the contact areas of neighboring teeth. Oncethe scanning process is completed, a scanning system (not shown in FIGS)will assemble the plurality of scans into a digital model (also referredto as a “dental model” or “digital dental model” herein) of thepreparation tooth and its surrounding and opposing teeth. The dentalmodel can be used to design a restoration to be used on the preparationtooth. For example, a dental restoration design program may process anddisplay the dental model in a user interface on a user device. A user(e.g., a design technician) operating on the user device can view thedental model and design or refine a restoration model based on thedental model.

In some embodiments, the present system may automatically recognizedental information and/or features from the dental model representing atleast a portion of a patient's dentition and display the recognizeddental information and/or features before the user starts doing manualdesign. For example, the present system may identify and label lower andupper jaws, prepared and opposing jaws, the number of tooth on which thepreparation to be used, or the type of restoration to be designed. Inother examples, the present system may also detect features, e.g.,cusps, or margin line of the dentition for the user. Therefore, the userdoes not have to start the design from scratch and with the recognizeddental information and features the user can accomplish the design moreeasily and fast. Further, in some situation, the automaticallyrecognized dental information and features may function as check toguarantee the user performs the design appropriately.

In some embodiments, the present system may feed the recognized dentalinformation and features to other auto-design programs for generating arestoration auto-proposal for users. For example, the auto-designprograms can search a tooth library for the library tooth arch form thatbest matches the dentition in the dental model and position itautomatically. By combining the recognized dental information andfeatures into the proposed library tooth arch form, the present systemfurther facilitates the users' design.

FIG. 1 shows a block diagram of a system 100 for recognizing dentalinformation from scan data of patients' dentitions and for designingdental restorations in accordance with some embodiments of the presentdisclosure. System 100 includes a dental restoration server 101, adesign device 103 operated by a user 147, a client device 107 and ascanner 109 operated by a client 175, and a third party server 151connected by a network 105. Only one dental restoration server 101, onedesign device 103, one client device 107 and one third party server 151are shown in FIG. 1 in order to simplify the description. Embodiments ofsystem 100 can have multiple dental restoration servers 101, designdevices 103, client devices 107 and third party servers 151 connected tothe network 105. Likewise, the functions performed by the variousentities of FIG. 1 may differ in different embodiments.

Network 105 enables communications among dental restoration server 101,design device 103, client device 107 and third party server 151. In someembodiments, network 105 uses standard communications technologiesand/or protocols. For example, network 105 may be a conventional type ofnetwork, wired or wireless, and may have any number of configurationssuch as a star configuration, token ring configuration or otherconfigurations known to one skilled in the related art. In anotherembodiment, the entities can use custom and/or dedicated datacommunications technologies instead of, or in addition to, the onesdescribed above.

In some embodiments, network 105 comprises one or more of a local areanetwork (LAN), a wide area network (WAN) (e.g., the Internet), and/orany other interconnected data path across which multiple devicescommunicate. In another embodiment, network 105 is a peer-to -peernetwork. Network 105 is coupled to or includes portions of atelecommunications network for sending data in a variety of differentcommunication protocols. For example, network 105 is a 3G network or a4G network. In yet another embodiment, the network 105 includesBluetooth communication networks or a cellular communications networkfor sending and receiving data such as via short messaging service(SMS), multimedia messaging service (MMS), hypertext transfer protocol(HTTP), direct data connection, Wireless application protocol (WAP),email, etc. In yet another embodiment, all or some of the links in thenetwork 105 are encrypted using conventional encryption technologiessuch as secure sockets layer (SSL), secure HTTP and/or virtual privatenetworks (VPNs).

In some embodiments, network 105 is not a traditional network, but acloud utilizing cloud computing techniques. Network/cloud 105 canincorporate any cloud servers. For example, dental restoration server101 can be a cloud server incorporated in cloud 105. Third party server151 can also be a cloud server included in cloud 105. By incorporatingthese servers, cloud 105 can provide cloud services to design device103, and/or client device 107 by utilizing cloud computing techniques.

Dental restoration server 101 receives dental restoration requests fromclient device 107 operated by client 175 such as a human client. In someembodiments, the dental restoration requests include scan dental modelsgenerated by scanner 109. In other embodiments, client 107 may sendphysical model or impression of a patient's teeth along with therequests to dental restoration server 101 and the digital dental modelcan be created accordingly on the server side 101, e.g., by anadministrator or technician operating on server 101. Dental restorationserver 101 creates and manages dental restoration cases based upon thereceived requests from client 107. For example, dental restorationserver 101 may assign the created cases to appropriate design device 103or third party server 151 to design dental restoration according to theclient's requests. When the cases have been completed, dentalrestoration server 101 may route the complete design back to client 107.In some embodiments, dental restoration server 101 may be incorporatedin cloud 105 to provide dental restoration services described herein.

In some embodiments, dental restoration server 101 can train deep neuralnetworks for automatic recognition of dental information from dentalmodels and identifies dental information of dental models associatedwith dental restoration cases or requests using the previously traineddeep neural networks. For example, the dental information may includelower and upper jaws, prepared and opposing jaws, tooth numbers,restoration types such as crown, inlay, bridge and implant, etc.Additional examples of dental information may include dental features(e.g., buccal and lingual cusps, occlusal surface, buccal and lingualarcs, etc.), margin lines, etc. In the illustrated embodiment, dentalrestoration server 101 includes a training module 120 and a scanrecognition module 125 a to perform the training of the deep neuralnetworks and the automatic dental information recognition, respectively.The scan recognition module 125 a, and 125 b, 125 c, 125 d (which aredescribed below with reference to corresponding devices or server) maybe also individually or collectively referred to as the scan recognitionmodule 125.

Dental restoration server 101 can have one or more deep neural networks,which can be part of training module 120 and/or qualitative evaluationmodule 135. Alternatively, the one or more deep neural networks can bean independent module residing within dental restoration server 101,remotely, or distributedly.

Dental restoration server 101 also includes a database 150 to store datarelated to the deep neural networks and the identified dentalinformation associated with the dental models. Dental restoration server101 may then feed the automatically identified dental information of thedental models to design device 103 or third party server 151 forfacilitating the restoration design. Database 150 can also be remotelylocated from dental restoration server 101 or be distributedly located.In some embodiments, dental restoration server 101 may send theidentified dental information of the dental models to client device 107for the client's 175 review. Other embodiments of dental restorationserver 101 may include different and/or additional components. Moreover,the functions may be distributed among the components in a differentmanner than described herein. Furthermore, system 100 may include aplurality of dental restoration servers 101 and/or other devicesperforming the work for a plurality of requesting clients 175.

Client device 107 can be an electronic device used by a human client 175to perform functions such as receiving and/or reviewing scan dentalmodels from scanner 109, submitting new dental restoration requestsincluding dental models to dental restoration server 101 for designand/or fabrication, receiving and/or reviewing finished dentalrestoration model design from dental restoration server 101 throughnetwork 105, or receiving and/or checking identified dental informationof the dental models. For example, client device 107 may be a smartphone, or a tablet, notebook, or desktop computer. Client device 107includes and/or interfaces with a display device on which human client175 may view the dental models, review the identified dental informationof the dental models, or review complete dental restoration design. Inaddition, client device 107 provides a user interface (UI), such asphysical and/or on-screen buttons, with which human client 175 mayinteract with client device 107 to perform functions such as submittinga new dental restoration request, receiving and reviewing identifieddental information associated with dental models, receiving andreviewing a completed dental restoration design, etc. In someembodiments, client device 107 may include a scan recognition module 125c for automatic recognition of dental information associated with thedental models. Therefore, the device 107 can directly identify thedental information of the dental models for client 175 to review andcheck.

Scanner 109 may be any type of device for scanning a prepared tooth andits surroundings or a dental impression. Scanner 109 can generate adigital file of the scanned tooth and its surroundings or a teethimpression, and transmit the file to client device 107. For example, thedigital file includes scan data and may represent a digital dentalmodel. As described above, the dental model can be used by client 107 tocreate and send a dental restoration request to dental restorationserver 101 for design and/or fabrication. In an alternative embodiment,client 175 can use the dental model to design the dental restoration onclient device 107 by own.

Design device 103 may be interacted by user 147 to design dentalrestoration requested by client 107. In some embodiments, design device103 may be a smart phone, or a tablet, notebook, or desktop computer.User 147 may be a human operator, dental technician or designer, etc.Design device 103 may receive dental restoration design assignment fromdental restoration server 101 and perform the design accordingly. Adesign software (not shown in FIG. 1) used for digital design of thedental restoration can be installed in design device 103. The designsoftware may provide library-based automatic dental restoration proposalfor the user 147 to accelerate and simplify the design process. In someembodiments, design device 103 may include a scan recognition module 125b for automatic recognition of dental information associated with thedental models. With the dental information (e.g., lower and upper jaws,prepared and opposing jaws, tooth numbers, restoration types such ascrown, inlay, bridge and implant, buccal and lingual cusps, occlusalsurface, buccal and lingual arcs, margin lines, etc.) being recognized,the design software can incorporate the dental information into theauto-proposal process to provide faster and better library-basedrestoration proposals.

The third party server 151 may be any one or more servers or devicesproviding dental restoration design to dental restoration server 101through network 105. In some embodiments, third party server 151 may berequired to conduct an agreement with dental restoration server 101. Insome embodiments, third party server 151 includes computing devicesequipped with the same or different design software (not shown inFIG. 1) from the software installed in design device 103. In theillustrated embodiment, third party server 151 may install a scanrecognition module 125 d for performing the dental informationrecognition function to facilitate the design. In some embodiments,third party server 151 may be included in the cloud 105 to providedental restoration design and/or fabrication services.

In some embodiments, dental restoration server 101 trains deep neuralnetworks to perform qualitative evaluations of restoration designs. Forexample, the system may perform qualitative evaluations of one or moreaspects of a restoration design, such as the margin line fit, contactsurfaces with adjacent teeth, occlusion with the teeth of the antagonistjaw, and contour of the restoration design. In the illustratedembodiment, dental restoration server 101 includes training module 120and a qualitative evaluation module 135 to perform the training of thedeep neural networks and the automatic qualitative evaluation,respectively.

Computing System Architecture

The entities shown in FIG. 1 are implemented using one or more computingdevices. FIG. 2 is a high-level block diagram of a computing device 200for acting as dental restoration server 101, design device 103, thirdparty server 151, and/or client device 107, or a component of the above.Illustrated are at least one processor 202 coupled to a chipset 204.Also coupled to chipset 204 are a memory 206, a storage device 208, agraphics adapter 212, and a network adapter 216. A display 218 iscoupled to the graphics adapter 212. In some embodiments, thefunctionality of the chipset 204 is provided by a memory controller hub220 and an I/O controller hub 222. In another embodiment, memory 206 iscoupled directly to processor 202 instead of chipset 204. Storage device208 is any non-transitory computer-readable storage medium, such as ahard drive, compact disk read-only memory (CD-ROM), DVD, or asolid-state memory device. Memory 206 holds instructions and data usedby processor 202. Graphics adapter 212 displays images and otherinformation on the display 218. Network adapter 216 couples the computersystem 200 to network 105.

As is known in the art, computing device 200 can have different and/orother components than those shown in FIG. 2. In addition, computingdevice 200 can lack certain illustrated components. Moreover, storagedevice 208 can be local and/or remote from the computing device 200(such as embodied within a storage area network (SAN)).

As is known in the art, computing device 200 is adapted to executecomputer program modules for providing functionality described herein.As used herein, the term “module” refers to computer program logicutilized to provide the specified functionality. Thus, a module can beimplemented in hardware, firmware, and/or software. In some embodiments,program modules such as training module 120 and the scan recognitionmodule 125 are stored on the storage device 208, loaded into memory 206,and executed by processor 202.

Prosthesis Generation Using Deep Neural Network

FIG. 3 illustrates a dental prosthesis generation process 300 using adeep neural network (DNN). Process 300 starts at 305 where a dentitionscan data set is received or ingested into a database such as database150. The dentition scan data set can include one or more scan data setsof real patient's dentitions with dental preparation sites andtechnician-generated (non-DNN generated) dental prostheses created forthose preparation sites. A dental preparation site (also referred to asa tooth preparation or a prepared tooth) is a tooth, a plurality ofteeth, or an area on a tooth that has been prepared to receive a dentalprosthesis (e.g., crown, bridge, inlay, etc.). A technician or a non-DNNgenerated dental prosthesis is a dental prosthesis mainly designed by atechnician. Additionally, a technician-generated dental prosthesis canbe designed based on a dental template library having a plurality ofdental restoration templates. Each tooth in an adult mouth can have oneor more dental restoration templates in the dental template library.More detail on the dental restoration library is provide below.

In some embodiments, the received dentition scan data set with dentalpreparation sites can include scan data of real patients' dentitionhaving one or more dental preparation sites. A preparation site can bedefined by a preparation margin. The received dentition scan data setcan also include scan data of dental prostheses once they are installedon their corresponding dental preparation sites. This data set can bereferred to as a dental prosthesis data set. In some embodiments, thedental prosthesis data set can include scan data of technician-generatedprostheses before they are installed.

In some embodiments, each dentition scan data set received mayoptionally be preprocessed before using the data set as input of thedeep neural network. Dentition scan data are typically 3D digital imageor file representing one or more portions of a patient's dentition. The3D digital image (3D scan data) of a patient's dentition can be acquiredby intraorally scanning the patient's mouth. Alternatively, a scan of animpression or of a physical model of the patient's teeth can be made togenerate the 3D scan data of a patient's dentition. In some embodiments,the 3D scan data can be transformed into a 2D data format using, forexample, 2D depth maps and/or snapshots.

At 310, a deep neural network can be trained (by training module 120 forexample) using a dentition scan data set having scan data of real dentalpreparation sites and their corresponding technician-generated dentalprostheses—post installation and/or before installation. The abovecombination of data sets of real dental preparation sites and theircorresponding technician-generated dental prostheses can be referred toherein as a technician-generated dentition scan data set. In someembodiments, the deep neural network can be trained using onlytechnician-generated dentition scan data set. In other words, thetraining data only contain technician-generated dental prostheses, whichwere created based on one or more dental restoration library templates.

A dental template of the dental restoration library can be considered tobe an optimum restoration model as it was designed with specificfeatures for a specific tooth (e.g., tooth #3). In general, there are 32teeth in a typical adult's mouth. Accordingly, the dental restorationlibrary can have at least 32 templates. In some embodiments, each toothtemplate can have one or more specific features (e.g., sidewall size andshape, buccal and lingual cusp, occlusal surface, and buccal and lingualarc, etc.) that may be specific to one of the 32 teeth. For example,each tooth in the restoration library is designed to include features,landmarks, and directions that would best fit with neighboring teeth,surrounding gingiva, and the tooth location and position within thedental arch form. In this way, the deep neural network can be trained torecognize certain features (e.g., sidewall size and shape, cusps,grooves, pits, etc.) and their relationships (e.g., distance betweencusps) that may be prominent for a certain tooth.

Training module 120 may train the deep neural network to recognize oneor more dentition categories are present or identified in the trainingdata set based on the output probability vector. For example, assumethat the training data set contains a large number of depth mapsrepresenting patients' upper jaws and/or depth maps representingpatients' lower jaws. Training module 120 can use the training data setto train the deep neural network to recognize each individual tooth inthe dental arch form. Similarly, the deep neural network can be trainedto map the depth maps of lower jaws to a probability vector includingprobabilities of the depth maps belonging to upper jaw and lower jaw,where the probability of the depth maps belonging to lower jaw is thehighest in the vector, or substantially higher than the probability ofthe depth maps belonging to upper jaw.

In some embodiments, training module 120 may train a deep neuralnetwork, using dentition scan data set having one or more scan data setsof real dental preparation sites and corresponding technician-generateddental prostheses, to generate full 3D dental restoration model. In thisway, the DNN generated 3D dental restoration model inherentlyincorporates one or more features of one or more tooth templates of thedental restoration library, which may be part of database 150.

Referring now to FIG. 4, which is a high-level block diagram showingstructure of a deep neural network (DNN) 400 according to someembodiments of the disclosure. DNN 400 includes multiple layers N_(i),N_(h,1), N_(h,1-1), N_(h,1), N₀, etc. The first layer N_(i) is an inputlayer where one or more dentition scan data sets can be ingested. Thelast layer N₀ is an output layer. The deep neural networks used in thepresent disclosure may output probabilities and/or full 3D restorationmodel. For example, the output can be a probability vector that includesone or more probability values of each feature or aspect of the dentalmodels belonging to certain categories. Additionally, the output can bea full 3D model of a dental restoration.

Each layer N can include a plurality of nodes that connect to each nodein the next layer N+1. For example, each computational node in the layerN_(h, 1-1) connects to each computational node in the layer N_(h,1). Thelayers N_(h,1), N_(h,1-1), N_(h,1), between the input layer N_(i) andthe output layer No are hidden layers. The nodes in the hidden layers,denoted as “h” in FIG. 4, can be hidden variables. In some embodiments,DNN 400 can include multiple hidden layers, e.g., 24, 30, 50, etc.

In some embodiments, DNN 400 may be a deep feedforward network. DNN 400can also be a convolutional neural network, which is a network that usesconvolution in place of the general matrix multiplication in at leastone of the hidden layers of the deep neural network. DNN 400 may also bea generative neural network or a generative adversarial network. In someembodiments, training module 120 may use training data set with labelsto supervise the learning process of the deep neural network. The labelsare used to map a feature to a probability value of a probabilityvector. Alternatively, training module 120 may use unstructured andunlabeled training data sets to train, in an unsupervised manner,generative deep neural networks that do not necessarily require labeledtraining data sets.

Training module 120 can train a deep neural network to generate a 3Dmodel of dental restoration using only the technician-designed dentitionscan data set. In this way, the DNN generated 3D dental prosthesis willinherently include one or more features of dental prosthesis designed bya human technician using the library template. In some embodiments,training module 120 can train the deep neural network to output aprobability vector that includes a probability of an occlusal surface ofa technician-generated dental prosthesis representing the occlusalsurface of a missing tooth at the preparation site or margin.Additionally, training module 120 can train a deep neural network togenerate a complete 3D dental restoration model by mapping the occlusalsurface having the highest probability and margin line data from thescanned dentition data to a preparation site. Additionally, trainingmodule 120 can train the deep neural network to generate the sidewall ofthe 3D dental restoration model by mapping sidewalls data oftechnician-generated dental prostheses to a probability vector thatincludes a probability of that one of the sidewalls matches with theocclusal surface and the margin line data from the preparation site.

Referring again to FIG. 3, to generate a new 3D model of a dentalprosthesis for a new patient, the new patient's dentition scan data(e.g., scanned dental impression, physical model, or intraoral scan)received and ingested at 315. In some embodiments, the new patient'sdentition scan data can be preprocessed to transform 3D image data into2D image data, which can make the dentition scan data easier to ingestby certain neural network algorithms. At 320, using the previouslytrained deep neural network, one or more dental features in the newpatient's dentition scan data are identified. The identified featurescan be a preparation site, the corresponding margin line, adjacent teethand corresponding features, and surrounding gingiva for example.

At 325, using the trained deep neural network, a full 3D dentalrestoration model can be generated based on the identified features at320. In some embodiments, the trained deep neural network can be taskedto generate the full 3D dental restoration model by: generating anocclusal portion of a dental prosthesis for the preparation site;obtaining the margin line data from the patient's dentition scan data;optionally optimizing the margin line; and generating a sidewall betweenthe generated occlusal portion and the margin line. Generating anocclusal portion can include generating an occlusal surface having oneor more of a mesiobuccal cusp, buccal grove, distobuccal cusp, distalcusp, distobuccal groove, distal pit, lingual groove, mesiolingual cusp,etc.

The trained deep neural network can obtain the margin line data from thepatient's dentition scan data. In some embodiments, the trained deepneural network can optionally modify the contour of the obtained marginline by comparing and mapping it with thousands of other similar marginlines (e.g., margin lines of the same tooth preparation site) havingsimilar adjacent teeth, surrounding gingiva, etc.

To generate the full 3D model, the trained deep neural network cangenerate a sidewall to fit between the generated occlusal surface andthe margin line. This can be done by mapping thousands of sidewalls oftechnician-generated dental prostheses to the generated occlusal portionand the margin line. In some embodiments, a sidewall having the highestprobability value (in the probability vector) can be selected as a basemodel in which the final sidewall between occlusal surface and themargin line will be generated.

FIG. 5 illustrates example input and output of the trained deep neuralnetwork 400 (e.g., GAN) in accordance with some embodiments of thepresent invention. As shown, an input data set 505 can the new patient'sdentition scan having a preparation site 510. Using the trained one ormore deep neural network 400, dental restoration server 101 can generatea (DNN-generated) 3D model of a dental restoration 515. DNN-generateddental prosthesis 515 includes an occlusal portion 520, a margin lineportion 525, and a sidewall portion 530. In some embodiments, the deepneural network can generate the sidewall for prosthesis 515 by analyzingthousands of technician-generated dental prostheses—which were generatedbased on one or more library templates—and mapping them to preparationsite 510. Finally, the sidewall having the highest probability value canbe selected as a model to generate sidewall 530.

FIG. 6A is a high-level block diagram illustrating a structure of agenerative adversarial network (GAN network) 600 that can be employed toidentify and model dental anatomical features and restorations, inaccordance with some embodiments of the present disclosure. On a highlevel, GAN network 600 uses two independent neural networks against eachother to generate an output model that is substantiallyindistinguishable when compared with a real model. In other words, GANnetwork 600 employs a minimax optimization problem to obtain convergencebetween the two competing neural networks. GAN network 600 includes agenerator neural network 610 and a discriminator neural network 620. Insome embodiments, both neural network 610 and discriminator neuralnetwork 620 are deep neural networks structured to perform unstructuredand unsupervised learning. In GAN network 600, both the generatornetwork 610 and the discriminator network (discriminating deep neuralnetwork) 620 are trained simultaneously. Generator network 610 istrained to generate a sample 615 from the data input 605. Discriminatornetwork 620 is trained to provide a probability that sample 615 belongsto a training data sample 630 (which comes from a real sample, real data625) rather than one of the data sample of input 605. Generator network610 is recursively trained to maximize the probability thatdiscriminator network 620 fails to distinguish apart (at 635) a trainingdata set and an output sample generated by generator 610.

At each iteration, discriminator network 620 can output a loss function640, which is used to quantify whether the generated sample 615 is areal natural image or one that is generated by generator 610. Lossfunction 640 can be used to provide the feedback required for generator610 to improve each succeeding sample produced in subsequent cycles. Insome embodiments, in response to the loss function, generator 610 canchange one or more of the weights and/or bias variables and generateanother output

In some embodiments, training module 120 can simultaneously train twoadversarial networks, generator 610 and discriminator 620. Trainingmodule 120 can train generator 610 using one or more of a patient'sdentition scan data sets to generate a sample model of one or moredental features and/or restorations. For example, the patient'sdentition scan data can be 3D scan data of a lower jaw including aprepared tooth/site and its neighboring teeth. Simultaneously, trainingmodule 120 can train discriminator 620 to distinguish a generated a 3Dmodel of a crown for the prepared tooth (generated by generator 610)against a sample of a crown from a real data set (a collection ofmultiple scan data set having crown images). In some embodiments, GANnetworks are designed for unsupervised learning, thus input 605 and realdata 625 (e.g., the dentition training data sets) can be unlabeled.

FIG. 6B is a flow chart of a method 650 for generating a 3D model of adental restoration in accordance with some embodiments of the presentdisclosure. Method 650 can be performed by one or more modules of dentalrestoration server 101 such as training module 120. The instructions,processes, and algorithms of method 650 may be stored in memory 206 ofcomputing device 200, and when executed by processor 202, they enablecomputing device 200 to perform the training of one or more deep neuralnetworks for generating 3D dental prostheses. Some or all of theprocesses and procedures described in method 650 may be performed by oneor more other entities or modules within restoration server 101 orwithin another remote computing device. In addition, one or more blocks(processes) of method 650 may be performed in parallel, in a differentorder, or even omitted.

At 655, training module 120 may train a generative deep neural network(e.g., GAN generator 610) using unlabeled dentition data sets togenerate a 3D model of a dental prosthesis such as a crown. In oneembodiment, labeled and categorized dentition data sets may be used, butnot necessary. The generative deep neural network may reside in trainingmodule 120 or in a separate and independent neural network module,within or outside of dental restoration server 101.

At 660, and at substantially the same, training module 120 may alsotrain a discriminating deep neural network (e.g., discriminator 620) torecognize that the dental restoration generated by the generative deepneural network is a model versus a digital model of a real dentalrestoration. In the recognition process, the discriminating deep neuralnetwork can generate a loss function based on comparison of a realdental restoration and the generated model of the dental restoration.The loss function provides a feedback mechanism for the generative deepneural network. Using information from the outputted loss function, thegenerative deep neural network may generate a better model that canbetter trick the discriminating neural network to think the generatedmodel is a real model.

The generative deep neural network and the discriminating neural networkcan be considered to be adverse to each other. In other words, the goalof the generative deep neural network is to generate a model that cannotbe distinguished by the discriminating deep neural network to be a modelbelonging a real sample distribution or a fake sample distribution (agenerated model). At 665, if the generated model has a probability valueindicating that it is most likely a fake, the training of both deepneural networks repeats and continues again at 655 and 660. This processcontinues and repeats until the discriminating deep neural networkcannot distinguish between the generated model and a real model. Inother words, the probability that the generated model is a fake is verylow or that the probability that the generated model belong to adistribution of real samples is very high.

Once the deep neural networks are trained, method 600 is ready togenerate a model of a dental restoration based on the patient'sdentition data set, which is received at 670. At 675, a model of thepatient's dentition data set is generated using the received patient'sdentition data set.

FIG. 7 illustrates a process 700 to generate a new natural dentitiondata set that can be used to train deep neural networks using archsegmentation in accordance with some embodiments of the presentdisclosure. Process 700 starts at 705 where one or more dentition scandata sets are received and ingested into database 150. In someembodiments, data ingestion of 3D format image can include transformingthe 3D data into 2D data formats, which can be used to train a deepneural network to recognize various dental features, includingindividual tooth identification. At 710, a partial or full archsegmentation is performed on the received dentition scan data. The archsegmentation process can be useful for dentition identification andcategorization. In other words, with the arch segmentation capability,dental restoration server 101 can use the trained deep neural network toidentify the location and features of a preparation site and the toothidentifier (e.g., tooth #3) that a generated dental prosthesis shouldmimic. In another example, with arch segmentation capability, dentalrestoration server 101 can identify a scan of any individualtooth—without any data of neighboring teeth and/or surrounding gingiva.

In some embodiments, the arch segmentation process includes identifyingand segmenting the dentition scan data into a plurality of individualtooth data components. For example, given a partial dentition scanhaving 4 teeth (number 1-4), the segmentation process can flag and/orseparate out scan data into 4 separate data components. Each componentrepresents scan data for each tooth. Thus, the segmentation process canmodify the partial dentition scan data such that data for any of thefour individual teeth can be selected, removed, and/or replaced. At 715,one of the data component for a tooth (e.g., tooth #2) can be deletedfrom the original scan dentition data of the 4-tooth arch form. In someembodiments, the data component for the deleted tooth can be replacedwith a digitally created/fabricated preparation site or margin. Thus,the new dentition scan data for the new arch form (1 toothextracted/deleted) includes 3 teeth and one preparation site.

In some embodiments, one of the useful applications for archsegmentation is to have the capability to generate an entirely newnatural dentition data set (at 720) to train deep neural networks togenerate naturally looking 3D model of dental prosthesis. A naturaldentition scan data set as used herein has two main components. Thefirst component is a data set that includes scanned dentition data ofpatients' natural teeth (ideally a full arch scan of top and bottomjaws). Data in the first component includes all of the patients' teethin its natural and unmodified digital state. The second component of thenatural dentition scan data is a missing-tooth data set with one or moreteeth removed from the scanned data. In place of the missing-tooth, aDNN generated preparation site can be placed at the site of themissing-tooth. This process generates two sets of dentition data: a fulland unmodified dentition scan data of patients' natural teeth; and amissing-tooth data set in which one or more teeth are digitally removedfrom the dentition scan data.

At 725, one or more deep neural networks are trained using the newlycreated natural training scan data set. Once a deep neural network istrained using the newly created natural training scan data set, it canbe used to generate a natural 3D dental prosthesis. It should be notedthat one of the main differences between dental prosthesis generationprocesses 300 and 700 is that the DNN-generated 3D model generated fromprocess 300 is based on technician-generated dental prosthesis (which inturn is based off a dental restoration template) and the DNN-generated3D model generated from process 700 is based of patients' naturaldentition features. The former DNN-generated 3D model can be consideredto be more technically perfect than its natural based counterpart sinceit is created based on a carefully engineered library template.

Customized Training and Model Generation

FIG. 8 illustrates a process 800 for customizing the training data setin order to generate a customized DNN-generated dental prosthesis inaccordance with some embodiments of the present disclosure. At 805, apatient's dentition scan data and personal data are received. Personaldata may include the patient's age, gender, ethnicity, diet, lifestyle,and other information that can provide insight on the patient'sdentition or can create groups or classes of patient. At 810, a naturaltraining data set is generated based on the patient's personal data. Acustomized training data set from people with similar age, diet, andlifestyle can lead to a more desirable DNN-generated 3D dentalprosthesis. For example, a septuagenarian patient may want aDNN-generated crown that looks similar to other septuagenarians' naturaltooth so that the DNN-generate crown does not look artificial onceinstalled. In another example, a patient from Southeastern United Statesand who smokes tobacco products may want a DNN-generated crown thatlooks similar to other people in Southeastern United States who alsosmoke tobacco products. Accordingly, each dentition data set can becategorized and classified by one or more personal data. In someembodiments, the dentition data set classification can be done by dentalrestoration server 101 or manually.

A natural dentition training data set is created by selecting dentitionscan data that match with the patient's personal data such as age,gender, diet, lifestyle, etc. As previously mentioned, a naturaltraining data set has two main components. The first component is acollection of scanned dentition data of patients' natural teeth (ideallya full arch scan of top and bottom jaws). Using the above example, onlydentition scan data of people in Southeastern United States will beused. And if available, only dentition scan data of tobacco users willbe used. The second component of the natural dentition scan data is amissing-tooth data set with one or more teeth removed from the scanneddata. In place of the missing-tooth, a DNN generated preparation sitecan be placed at the site of the missing-tooth. Again, this processgenerates two sets of dentition data for the natural training data set:a full and unmodified dentition scan data of patients' natural teeth;and a missing-tooth data set in which one or more teeth are digitallyremoved from the dentition scan data, which can be referred to herein asa natural dental prosthesis data set.

At 815, the natural training data set is used to train a deep neuralnetwork to generate a full 3D model of a dental prosthesis. At 820, thetrained deep neural network can generate the full 3D model of the dentalprosthesis based on received patient's dentition scan data. Although a3D dental prosthesis generated by process 300 is very good from adentition perspective, the 3D dental prosthesis generated by process 800using customized training data set can be more desirable to the patientand may look more natural because of inherent imperfections (e.g., lessdistinctive cusps and grooves and more blended features).

FIG. 9A illustrates an example DNN-generated crown 900 using process300, which uses a DNN that has been trained using technician-generatedcrowns. As previously mentioned, technician-generated crowns are more“perfect” looking because they are based on a carefully engineereddental template of dental restoration library. As shown, crown 900 hasdistinctive grooves and cusp. FIG. 9B illustrates an exampleDNN-generated crown 950 is generated using process 700 or 800, whichuses a DNN that has been trained using a natural dentition training dataset. As shown, crown 950 has blended features (i.e., cusps and groovesare not distinctive and blended). Although crown 950 may appear to beless perfect than crown 900, it may still be desirable over crown 900because it may fit in better with the surrounding teeth. For example, ifsubstantially all of the septuagenarian's teeth are worn, installingcrown 900 would immediately stand out. Accordingly, in this case, crown950 may be more desirable for the septuagenarian patient.

FIGS. 10A-B illustrate an exemplary user interface 1000 to generate ablended crown by transferring/combining one or more features of crown950 into crown 900 in accordance with some embodiments of the presentdisclosure. User interface 1000 includes a natural crown window 1005, anengineered-crown window 1010, a blended-crown window 1015, aboundary/contour slide bar 1020, and an anatomy slide bar 1025. Asshown, natural crown window displays DNN-generated crown using process700 or 800. Engineered-crown (e.g., crowns generated usingtechnician-generated crown data) window 1010 displays DNN-generatedcrown using process 300, and blended-crown window displays a crownhaving blended features from both natural and engineered crown.

To add more contour and/or more features to the natural crown in window1005, the user can slide the bar on slide bar 1020 and/or slide bar 1025and the trained DNN will automatically transfer one or more features ofthe contour and anatomy of the engineered-crown in window 1010 to thenatural crown in window 1005. The result of the feature transferfunction is the blended crown 1025 or 1030. As shown in FIG. 10B, amaximum amount of anatomy is selected using the slide bar. Accordingly,blended crown 1030 has very similar or identical features as engineeredcrown.

The above description is included to illustrate the operation of thepreferred embodiments and is not meant to limit the scope of theinvention. The scope of the invention is to be limited only by thefollowing claims. From the above discussion, many variations will beapparent to one skilled in the relevant art that would yet beencompassed by the spirit and scope of the invention.

The foregoing description of the embodiments of the present inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the present invention tothe precise form disclosed. Many modifications and variations arepossible in light of the above teaching. It is intended that the scopeof the present invention be limited not by this detailed description,but rather by the claims of this application. As will be understood bythose familiar with the art, the present invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. Likewise, the particular naming and division ofthe modules, routines, features, attributes, methodologies and otheraspects are not mandatory or significant, and the mechanisms thatimplement the present invention or its features may have differentnames, divisions and/or formats.

Furthermore, as will be apparent to one of ordinary skill in therelevant art, the modules, routines, features, attributes, methodologiesand other aspects of the present invention can be implemented assoftware, hardware, firmware or any combination of the three. Also,wherever a component, an example of which is a module, of the presentinvention is implemented as software, the component can be implementedas a standalone program, as part of a larger program, as a plurality ofseparate programs, as a statically or dynamically linked library, as akernel loadable module, as a device driver, and/or in every and anyother way known now or in the future to those of ordinary skill in theart of computer programming.

Additionally, the present invention is in no way limited toimplementation in any specific programming language, or for any specificoperating system or environment. Accordingly, the disclosure of thepresent invention is intended to be illustrative, but not limiting, ofthe scope of the present invention, which is set forth in the followingclaims.

What is claimed is:
 1. A computer-implemented method for generatingdental restoration associated with dental model of dentition, the methodcomprising: training, by one or more computing devices, a deep neuralnetwork to generate a first 3D dental prosthesis model using a trainingdata set; receiving, by the one or more computing devices, a patientscan data representing at least a portion of a patient's dentition; andgenerating, using the trained deep neural network, the first 3D dentalprosthesis model based on the received patient scan data.
 2. The methodof claim 1, wherein the training data set comprises a dentition scandata set with preparation site data and a dental prosthesis data set,wherein the dental prosthesis data set comprises scanned prosthesis dataassociated with each preparation site in the dentition scan data set. 3.The method of claim 2, wherein the dental prosthesis data set comprisesscanned data of real crowns created based on a library tooth template.4. The method of claim 3, wherein the library comprises 32 toothtemplates.
 5. The method of claim 4, wherein the real crowns are createdby technicians using the 32 tooth templates.
 6. The method of claim 2,wherein the dentition scan data set with preparation site data comprisesscanned data of real preparation sites from patients' scanneddentitions.
 7. The method of claim 1, wherein the dental prosthesiscomprises a crown, an inlay, a bridge or an implant
 8. The method ofclaim 1, wherein the training data set comprises a natural dentitionscan data set with digitally fabricated preparation site data and anatural dental prosthesis data set, wherein the dental prosthesis dataset comprises segmented tooth data associated with each digitallyfabricated preparation site in the dentition scan data set.
 9. Themethod of claim 8, wherein the segmented tooth data are generated bysegmenting tooth data from the natural dentition scan data set.
 10. Themethod of claim 2, further comprising: training a second deep neuralnetwork to generate a second 3D dental prosthesis model using a naturaldentition scan data set with digitally fabricated preparation site dataand a natural dental prosthesis data set, wherein the natural dentalprosthesis data set comprises segmented tooth associated with eachdigitally fabricated preparation site in the dentition scan data set;and generating, using the second deep neural network, the second 3Ddental prosthesis model based on the received patient scan data.
 11. Themethod of claim 10, further comprising blending together features of thefirst and second 3D dental prosthesis models to generate a blended 3Ddental prosthesis model.
 12. The method of claim 10, further comprising:receiving a patient's profile information; and selecting one or moredentition scan data sets that match with the patient's profileinformation, wherein the natural dentition scan data only includematched dentition scan data sets.
 13. A computer program productcomprising a computer-readable storage medium having computer programlogic recorded thereon for enabling a processor-based system to generatea 3D dental prosthesis model, the computer program product comprising: afirst program logic module for enabling the processor-based system totrain a deep neural network to generate a first 3D dental prosthesismodel using a training data set; a second program logic module forenabling the processor-based system to receive a patient scan datarepresenting at least a portion of a patient's dentition; and a thirdprogram logic module for enabling the processor-based system to generatethe first 3D dental prosthesis model based on the received patient scandata.
 14. The computer program product of claim 13, wherein the trainingdata set comprises a dentition scan data set with preparation site dataand a dental prosthesis data set, wherein the dental prosthesis data setcomprises scanned prosthesis data associated with each preparation sitein the dentition scan data set.
 15. The computer program product ofclaim 14, wherein the dental prosthesis data set comprises scanned dataof real crowns created based on a library tooth template.
 16. Thecomputer program product of claim 15, wherein the library comprises 32tooth templates.
 17. The computer program product of claim 16, whereinthe real crowns are created by technicians using the 32 tooth templates.18. The computer program product of claim 14, wherein the dentition scandata set with preparation site data comprises scanned data of realpreparation sites from patients' scanned dentitions.
 19. The computerprogram product of claim 13, wherein the training data set comprises anatural dentition scan data set with digitally fabricated preparationsite data and a natural dental prosthesis data set, wherein the dentalprosthesis data set comprises segmented tooth data associated with eachdigitally fabricated preparation site in the dentition scan data set.20. The computer program product of claim 19, wherein the segmentedtooth data are generated by segmenting tooth data from the naturaldentition scan data set.
 21. The computer program product of claim 14,further comprises: a fourth program logic module for enabling theprocessor-based system to train a second deep neural network to generatea second 3D dental prosthesis model using a natural dentition scan dataset with digitally fabricated preparation site data and a natural dentalprosthesis data set, wherein the natural dental prosthesis data setcomprises segmented tooth associated with each digitally fabricatedpreparation site in the dentition scan data set; and a fifth programlogic module for enabling the processor-based system to generate, usingthe second deep neural network, the second 3D dental prosthesis modelbased on the received patient scan data.
 22. The computer programproduct of claim 21, further comprises a sixth program logic module forenabling the processor-based system to blend together features of thefirst and second 3D dental prosthesis models to generated a blended 3Ddental prosthesis model.